GuideBeginner
Vector Databases Fundamentals Guide
Learn how vector databases work under the hood, why you need them for RAG systems, and how to choose the right one for your project. This guide takes you from the fundamentals (architecture, indexing algorithms, essential features) to a hands-on implementation with ChromaDB and an integrative RAG project with 1,000+ documents and a FastAPI API. By the end, you will know how to implement vector storage and retrieval for production — and you will have the judgment to decide between ChromaDB, Pinecone, Weaviate, and Qdrant based on your real requirements.
- 67
- lessons
- 8
- modules
- English · Spanish
- available in
- Yes
- certificate
- Free
- access
Outcomes
What you'll be able to do
- Understand why RAG systems need vector databases (and when SQL, NoSQL, or numpy are enough)
- Explain how vector databases work internally: architecture, HNSW, IVF, and PQ indexing algorithms
- Identify essential features for production RAG: metadata filtering, hybrid search, multi-tenancy, batch operations
- Implement ChromaDB from setup to optimized similarity search with metadata filtering
- Build a complete RAG system: document ingestion, indexing, retrieval, and generation with FastAPI
- Compare Pinecone, Weaviate, Qdrant, and Milvus — features, pricing, and architecture trade-offs
- Apply a decision framework to choose the right vector database for any project
- Design production strategies: scaling, monitoring, backups, migrations, and cost optimization
Before you start
What you need to bring
It's for you if...
- AI Engineers building RAG systems who need to store and retrieve embeddings at scale
- Developers who completed the Embeddings Deep Dive Guide and want to implement vector storage for production
- Backend engineers evaluating vector database options (ChromaDB vs Pinecone vs Weaviate) for their AI projects
- Students preparing for Week 7-9 of the AI Engineering Bootcamp (RAG implementation modules)
Requirements and materials
- Embeddings Deep Dive Guide completed (Guide #6): how to generate embeddings, distance metrics, semantic search with numpy
- AI Semantics Guide completed (Guide #5): what vectors are, cosine similarity, keyword vs semantic search
- Python intermediate: functions, classes, async/await, pip packages
- Basic familiarity with REST APIs (requests, endpoints, JSON)
Content
The syllabus, module by module
Open any of them to see its lessons.
- Module 1: Why Vector Databases for AI Engineers
- Capsule 02: The core problem — RAG needs to find 5 docs among millions in milliseconds
- Why SQL/NoSQL don't work for semantic search
- Why numpy/pandas don't scale to production
- When you DO need a vector database
- When you DON'T need a vector database
- Trade-offs: Simplicity vs Performance vs Cost
- Module 1 Summary and Transition
- Module 2: How Vector Databases Work (Conceptual)
- Capsule 02: Vector Database Architecture (3 Layers)
- Capsule 03: Indexing Algorithms - Overview
- Capsule 04: HNSW - Hierarchical Navigable Small World
- Capsule 05: IVF - Inverted File Index
- Capsule 06: PQ - Product Quantization
- Capsule 07: HNSW vs IVF vs PQ comparison - Decision Framework
- Capsule 08: Why this matters for RAG - Module Summary
- Module 3: Essential Features for RAG
- Capsule 02: Metadata Filtering (Where Clauses)
- Capsule 03: Hybrid Search (Keyword + Semantic)
- Capsule 04: Multi-tenancy (Data isolation)
- Capsule 05: Batch Operations — the hidden cost of naive ingestion
- Capsule 06: Distance Metrics — the geometric decision almost nobody justifies
- Capsule 07: Observability — what to measure when nobody debugs with prints anymore
- Capsule 08: Features Comparison and Module Summary
- Module 4: ChromaDB Setup and Configuration
- Capsule 02: ChromaDB Installation and Setup
- Capsule 03: Collection Configuration — the three parameters almost nobody justifies
- Capsule 04: Metadata Filtering Implementation — from concept to code in ChromaDB
- Capsule 05: Batch Ingestion in ChromaDB — from concept to an operable pipeline
- Capsule 06: Query Optimization — where you win or lose real latency
- Capsule 07: Persistence and Durability — what happens when something breaks
- Capsule 08: Mini-Project - Document Search System
- Capsule 09: Embeddings with OpenAI — When to switch from the default
- Capsule 10: Document chunking — the whole-document problem
- Capsule 11: End-to-end RAG pipeline with ChromaDB
- Module 5: Vector Database Landscape for AI Engineers
- Capsule 02: Vector DB Provider Landscape
- Capsule 03: Managed vs Self-Hosted
- Capsule 04: Feature Comparison for RAG
- Capsule 05: Selection Costs and Trade-offs
- Capsule 06: When to Choose Each Option
- Capsule 07: Anti-patterns When Choosing a Vector DB
- Capsule 08: Project - Decision Tree for Choosing a Vector DB
- Module 6: Decision Matrix for AI Engineers
- Capsule 02: Requirements and Decision Criteria
- Capsule 03: Weights, Scoring, and Matrix Methodology
- Capsule 04: Practical Decision Matrix
- Capsule 05: Cost and ROI Analysis
- Capsule 06: Recommendations by Scenario
- Capsule 07: Real-World Decision Cases
- Capsule 08: Project - Decision Questionnaire
- Module 7: Production Considerations for RAG
- Capsule 02: Scaling Strategies
- Capsule 03: Monitoring and Observability for RAG Systems
- Capsule 04: Backup and Disaster Recovery
- Capsule 05: Security for RAG Systems
- Capsule 06: Cost and Performance Optimization for RAG
- Capsule 07: Zero-Downtime Migration (ChromaDB → Pinecone)
- Capsule 08: Project - Production Readiness Checklist
- Module 8: Capstone RAG Project with ChromaDB
- Capsule 02: Project Architecture
- Capsule 03: Ingestion Pipeline for 1,000+ documents
- Capsule 04: Retrieval, Generation, and API
- Capsule 05: Testing and Evaluation
- Capsule 06: Observability and Deployment
- Capsule 07: Final Hardening
- Capsule 08: Project - Production-Ready RAG
Where it fits
This guide is part of something bigger
It's studied inside these programs, with support and dates.
Common questions
What people usually ask
No limit. It's a free guide: come in whenever you like, as often as you like.
No. Modules run from easier to harder, but you can jump to the one you need. Progress is saved per lesson.
Whatever is needed is listed under “What you need to bring”, above. If nothing is listed there, you can start from zero.
In the Club's WhatsApp group, and every two weeks there's a live with an instructor where questions get worked through.
Yes. It's issued automatically once you finish every lesson, with a verifiable code you can share on LinkedIn.
No. This guide is self-paced with no dates. The bootcamp is live, by cohort, with work someone reviews.
Start whenever you like
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